Extraction, processing, production and display of geographic data
Zahra Rabiee Gaffar; Hossein Asakereh; Uones Khosravi
Abstract
Extended Abstract Introduction The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability ...
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Extended Abstract Introduction The Intergovernmental Panel on Climate Change (IPCC) has reported that climate change results in anomalies, fluctuations or trends in climatic elements, such as precipitation and temperature. In this study, we aim to investigate the decadal changes in the probability of different durations of precipitation in Iran over the past four decades (1977-2016). To achieve this goal, we used the third version of the Asfazari database. We defined a rainy day as a day when the precipitation is more than the average precipitation in a given place. The Markov chain method was employed to estimate the probability of precipitation duration from 1971 through 2016.Materials and MethodsWe adopted the daily data of 2188 stations under the supervision of Iran’s Meteorological Organization for the period 1971 through 2016. Accordingly, we estimated the probability of precipitation duration for 1-7 days for the entire period. We investigated the decadal changes in the probability of precipitation duration for the four study decades and compared them to the whole period under investigation. To understand the spatial features of these changes, we estimated the relationship between changes in the probability of precipitation duration for 1-7 days and spatial factors using multivariate regression models.Results and DiscussionOur findings revealed that as the duration of rainy days increased, the area affected by precipitation decreased. Therefore, the spatial distribution of the probability of precipitation duration for more than 7 days indicated the smallest area that received precipitation. The probability duration of precipitation lasting 4 days or more throughout Iran was very small, which can be attributed to the effects of local features on precipitation formation. The probability of 1-day precipitation for most regions of Iran was higher than other durations; however, there was only a probability of 1-day precipitation in half of Iran. The highest probability of precipitation duration occurred in the Caspian region, the only region that experienced all durations of precipitation, indicating the presence of various precipitation mechanisms in this area. The greatest probability of decadal changes was observed in the 1-7 day duration in the northern part of Iran, including the northwest to the east of the Caspian Sea and in the south of Alborz Mountain range. Additionally, the most changes in the probability durations of 1-7 days of precipitation in the south have been seen in Sistan and Baluchistan. The lowest probability of decadal changes was shown in large areas of the regions from the east, southeast, and southwest. Therefore, the changes in precipitation durations in the southern half of the regions were generally low; however, in the northern half, the changes were relatively significant.In general, during the four study decades, the relationship between changes in the probability of 1-7 day precipitation durations and spatial factors, particularly latitude, was positive. Thus, decreasing latitudes resulted in an increasing probability of 1-7 day precipitation.ConclusionThe most likely changes in precipitation duration were related to the western and eastern coast of the Caspian Sea and the northwestern region of Iran, as well as southern Alborz, where the probability of changes decreased. The least amount of possible changes was related to the south of Iran, where only two provinces, Sistan and Baluchistan, and Hormozgan, experienced the greatest change in the probability of one to seven days of precipitation. Thus, the possible changes in the spatial continuation of precipitation in the southern half of the country were primarily low. However, in the northern half, the possible changes in the duration of precipitation were more significant. changes in the duration of precipitation, along with changes in the intensity and frequency of precipitation, can have significant consequences in extreme events such as droughts and floods. Accurately depicting changes in precipitation duration can be helpful in addressing problems concerning precipitation.
Geographic Data
Zahra Heydari monfared; Seyed Hossein Mirmousavi; Hossein Asakereh; Koohzad Raisipour
Abstract
Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis ...
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Extended Abstract
Introduction: Snow-cover changes and related phenomena (especially depth, snow water equivalent and snow density) have a fundamental role in mountainous environments and strongly affect water availability in downstream areas. In this way, the importance of correct and appropriate analysis is more visible. Due to the fact that most of the rainfall falls in the form of snow in mountainous areas, the management of snow resources in these areas is very important, and knowing the different aspects of variability and geographical patterns governing the phenomenon of snow is a scientific and practical need. It is considered special in water resources and in the agricultural sector. Thus, in the current research, the spatio-temporal patterns governing the annual average of snow density in different decades and the difference of each of the decades compared to the entire time period have been estimated and analyzed using spatial statistics methods.
Materials & Methods: The studied area with an area of about 151,771.91 square kilometers is located between 34°44' to 39°25' north latitude from the equator and 44°3' to 49°52' east longitude from the Greenwich meridian. In order to investigate the spatial autocorrelation changes of the average snow density in northwest Iran during the years 1982-2022 from the data obtained from the database of the European Center for Medium-Range Atmospheric Forecasting ECMWF4/ ERA5 based on daily data, and to identify and understand the spatial patterns of density Barf, based on statistical and graphic models have been used in the geographic information system environment. In the study of temporal-spatial changes of the average snow density of the region in different time periods including 4 decades ((1982-1992), (1992-2002), (2002-2012), (2012-2022)) and the whole period of 41 years (2022) -1982)), general Moran's I and Getis-Ord Gi* statistics were used. Also, in the current research, in order to investigate the effect of changes in Extreme snow precipitation on the amount of snow density in the northwest region, it has been done to determine the snow threshold. In order to estimate snow drift, a threshold was defined. Since the station snowfall amount data has a high dispersion, values above the mean cannot be accurate for defining the threshold of freezing snow. In this way, the 99th percentile index has been used to determine the snow threshold.
Results & Discussion: The aim of the current research is to investigate the spatial autocorrelation changes of the annual mean snow density in the northwest of Iran. For this purpose, the annual snow density data during the statistical period of 1982-2022 was obtained from the ECMWF/EAR5 database with a resolution of 0.25 x 0.25 degrees, and then divided into four ten-year periods. In order to analyze spatial autocorrelation changes, global Moran indices and hot spot analysis (Gettys-RDJ) were used at the significance level of 90, 95 and 99%. Also, in order to investigate the effect of extreme precipitation on changes in the level of snow density, the 99th percentile statistical index was used, and based on this index, the freezing threshold of each synoptic station in the region was determined during the last decade (2012-2022) and the interval the entire statistical period (1982-2002) was carried out. The results of the present research showed that in the studied area, snow density has spatial autocorrelation and a strong cluster pattern. With a density threshold less than 0.10 kg/m3, from the first decade to the end of the fourth decade, the area (number of pixels) and the amount of snow density in the northwest have decreased. The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly during the last decade of the study, and this has caused the snow density to increase relatively in the last decade compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has significantly decreased during the last four decades.
Conclusion: The evaluation of the temporal changes of snow density also strengthened the hypothesis of the occurrence of freezing snow precipitation leading to an increase in snow density in the months of cold seasons during the last decade. This point was confirmed by examining the statistical index of the 99th percentile of snowy days of each synoptic station in the region during the last decade (2009-2018) compared to the entire period of station statistics (2000-2018). The results of the analysis of the changes in precipitation in the 99th percentile showed that the amount of this type of precipitation has increased significantly in the last decade of the study and this has caused the snow density in the last decade to increase relatively compared to the first to third decades. However, in general, the amount of snow density in the entire northwest area has decreased significantly during the last four decades. Moran's statistic was used to explain the pattern governing snow density in northwest Iran. The results of Moran's index about the annual average of snow density showed that the values related to different time periods have a positive coefficient and are close to one, which indicates that the snow density data has spatial autocorrelation and has a cluster pattern. Also, the results of standard Z score and P-value confirmed the cluster significance of the spatial distribution of snow density in the northwest. Finally, the analysis of hot spots has been a clear confirmation of the continuation of concentration and clustering of snow density in northwest Iran in space with the increase of the time period, which mountainous areas have the first rank in the formation of hot clusters with a probability of 99%. have given.
Geographic Data
Hadiseh Sabzi Sorkhani; Abdollah Faraji; Hossein Asakereh
Abstract
Extended AbstractIntroductionClimatic conditions have a significant impact on human living conditions and comfort. From the earliest times, humans have reacted to fluctuations in weather conditions Therefore, it has designed its living environment in accordance with the climatic conditions. One ...
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Extended AbstractIntroductionClimatic conditions have a significant impact on human living conditions and comfort. From the earliest times, humans have reacted to fluctuations in weather conditions Therefore, it has designed its living environment in accordance with the climatic conditions. One of the most important measures in identifying a comfortable climate is to assess the conditions of the human living environment. Which determines the level of human comfort in the environment. Meteorological variables and bioclimatic indicators are needed to assess the conditions of human comfort in the environment.. Therefore, urban planners and regional planners need useful and at the same time practical indicators in order to optimize the environment and determine the best time for human comfort. Human comfort conditions provide very good information for planners by evaluating bioclimatic indicators. The information obtained from this research provides appropriate suggestions and strategies for improving the situation in each region. To know the range of comfort climate (optimal and optimal climate) can not be enough to describe the climatic elements of the region, including temperature, humidity, wind and radiation. Rather, the type of climate in terms of comfort or lack of thermal and climatic comfort should be determined quantitatively in the form of a general indicator and a combination of all these climatic elements. Many factors affect the tourism industry, one of the most important of which is the climate. Climate plays a role as one of the most important local resources in the tourism industry. Awareness of climatic comfort plays an important role in human life and activities, and physiological comfort is closely related to climatic factors. Therefore, the study of climatic parameters affecting the climatic comfort of work seems necessary.Gilan province is one of the most populous regions of the country and ranks first in the country in terms of population density. In addition to various economic activities, this province hosts millions of people from all over the country every year due to the existence of various attractions (especially the Caspian Sea). And it is one of the touristic provinces of the country And studying the climate comfort of this province can be an important step in planning for tourist reception and its requirements. In this study, the climatic comfort of Gilan province has been studied.Materals and methodsFor this purpose, climatic data from 11 synoptic stations (Rasht, Astara, Anzali, Deilman, Kiashahr, Lahijan, Manjil, Masouleh, Jirandeh, Talesh and Rudsar) including monthly average temperature, average maximum and minimum temperature in degrees Celsius, The monthly average of relative humidity as a percentage, the average of sunny hours and the number of rainy days during the statistical period of 1995 to 2020 have been received from the Meteorological Organization of the country. Then in SPSS software, Excel database was created And processed the data and calculated the average of all the mentioned parameters on a monthly basis And deficiencies were corrected And through the Tourism Climate Comfort Index (TCCI), the calculations are performed And after sorting and analyzing the climatic conditions, the study area has been studied Thus, based on the Tourism Climate Comfort Index (TCCI), zoning maps of the province were drawn in GIS software . Finally, the results are analyzed and interpreted as maps.Results and discussionApril, south of the province (Manjil), May, center, Caspian Sea coast, part of the northeast and south of the province (Rasht, Anzali, Lahijan, Deilman, Jirandeh), August, east of the province (Rudsar), September, center , East and part of the south of the province (Rasht, Rudsar, Deilman) and October, south of the province (Jirandeh) has pleasant and favorable conditions. All tourism activities are recommended to tourists.ConclusionThe results of the conducted investigations show the existence of various types of comfortable weather conditions for tourism in Gilan province. The months of May and June in most parts of the province have very favorable and pleasant conditions and the climatic conditions are suitable for all kinds of tourism activities. In the cold season of the year (January, February and December) in all parts of the province (especially the high areas such as Dilman and Masuleh, etc.), very unfavorable (cold) conditions prevail, and in the hot season of the year (July and August) the conditions are It is very unfavorable (heat) in most of the studied areas (especially coastal areas due to high humidity). All in all, in this research, all kinds of tourism activities have been targeted according to each season and each region
Hossein Asakereh; Skineh Khani Temeliyeh
Abstract
Extended Abstract
Introduction
As an influential element of climate, precipitation affects human activities and societies. It is thus considered to be the essence of any study conducted as a part of environmental and economic planning. Precipitation in Iran, especially in its west and southwest is ...
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Extended Abstract
Introduction
As an influential element of climate, precipitation affects human activities and societies. It is thus considered to be the essence of any study conducted as a part of environmental and economic planning. Precipitation in Iran, especially in its west and southwest is affected by thermal, dynamic, and thermodynamic low-pressure centers such as the Red Sea trough. The trough is an extension of Sudanese low-pressure with a central pressure of about 1006 hPa. The Red Sea is stretched in a southeast to northwest direction and thus connects tropical and subtropical regions. Considering the importance of the Red Sea low-pressure system for precipitation events in west and southwest Iran, any change in this system will affect precipitation patterns in the region. Analyzing the activity of this system and resulting precipitation in west and southwest Iran will thus provide more accurate understanding of the climate of this region.
Materials and methods
Environmental and precipitation data retrieved from Asfezari national database and atmospheric data (geopotential height) extracted from the European Center for Medium-Range Weather Forecasts (ECMWF) were utilized in the present study. A numerical algorithm was also used to identify the cyclones. The algorithm identified 459 cyclones in the statistical period.
Results and discussion
Time distribution of days in which the Red Sea trough is active showed increased activity in summer (198 days) especially August (99 days) and spring (178 days) especially April. However, the Red Sea trough showed decreased activity in autumn and winter. Activities of the Red Sea trough have shown a slightly decreasing but significant annual trend during the statistical period. A sharply and significantly decreasing slope can be observed in summer which results in a decreasing annual trend. Average daily precipitation of the study area in the statistical period ranged from 0 to 2.5 mm. The minimum average precipitation (less than 1 mm) was observed in 29.58% of the study area while maximum average precipitation (more than 2 mm) was observed in 3.64% of the study area. The largest part of the study area (66.87%) experienced an average daily precipitation of 1 to 2 mm. Moreover, 24.28% of the region with minimum precipitation (less than 1 mm) was located in the south and southwest of the study area. This indicates a relatively less severe impact of the Red Sea trough in this area. Around 70.88% of the study area has experienced a precipitation between 1 and 2 mm. Subtracting average daily precipitation recorded throughout the statistical period from the average daily precipitation occurring simultaneously with the activities of the Red Sea trough showed a positive anomaly (more than 0.4 mm) in the north and northeast of the study area. Therefore, it can be inferred that most of the precipitation in this area is originated over the Red Sea. It seems that the presence of the Zagros Mountains has also had a significant effect on precipitation in the study area. Areas with a negative anomaly (less than -0.4 mm) in which precipitation is not affected by the Red Sea trough include spatially scattered regions in Khuzestan, and Kohkiluyeh and Boyer-Ahmad provinces (0.74% of the study area). In other words, precipitation associated with the activity of the Red Sea trough was less than the total precipitation, and thus, most of the precipitation in these regions has other sources.
Conclusion
Results indicated that during the statistical period, minimum average daily precipitation has occurred in south, southwest, and northeast of the study area. Moreover, south and southwest of the study area experienced precipitation simultaneously with the activity of the Red Sea trough. The maximum precipitation in either cases (during the statistical period and also during the activity of the Red Sea trough) has been concentrated in parts of the northwest, west, and east of the study area (along the Zagros mountain range). Significant latitude difference between the north and south of the study area, and existence of the Zagros Mountains and consequently the heterogeneous topography have created two different zones in the study area experiencing minimum and maximum precipitation. In the presence of the Red Sea trough, a higher percentage of the study area experienced maximum precipitation. The frequency of days with more than one millimeter precipitation and their spatial distribution showed that under general conditions, the maximum precipitation has occurred in the north, northwest, west, and east covering 61.11% of the study area. Kurdistan province has recorded a maximum precipitation in more than 3500 days under the influence of different air masses. More than 73% of the factors associated with precipitation in Iran, especially in its northwest, west, and southwest are various synoptic systems (cyclones and short waves) entering the country from the Mediterranean with westerly winds. The minimum number of rainy days during the whole statistical period and also during the low-pressure activity of the Red Sea were also recorded in the southern and southwestern parts of the study area.
Geographic Data
Hossein Asakereh; Ava Gholami
Abstract
Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, ...
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Extended AbstractIntroductionAs global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. Materials & MethodsData collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. Results & DiscussionAppropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October. ConclusionSelecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.
Hossein Asakereh; hadis kiani
Abstract
Extended Abstract
Global warming and its consequence which occurs as climate change are of the world's major problems in the current century. Climate change and the warming of the earth have adverse effects on resources such as water, forests, pastures, agricultural land, industry and ultimately human ...
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Extended Abstract
Global warming and its consequence which occurs as climate change are of the world's major problems in the current century. Climate change and the warming of the earth have adverse effects on resources such as water, forests, pastures, agricultural land, industry and ultimately human life. The initial effect of climate change is on the atmospheric elements, particularly on the precipitation and temperature. Through evaluating long-term temperature trends we can be provided with a better insight as to how to plan for the upcoming years.
Temperature is one of the elements influencing this issue. That is why monitoring and assessing its behavior is very important to humans. Therefor the simulation of these variables can be vital to gain a perception of human future. There are various methods to simulate and predict climate variables. The most reliable one is using the data from the atmospheric general circulation models or GCM. The GCM models are only able to simulate the atmospheric general circulation data on large surfaces. The implementation of these models for long periods of time is time consuming and requires high processing speeds. To overcome this problem some simplifications should be done including a reduction in spatial resolution and removing some of the physical and thermodynamic processes at the micro scale. These simplifications increase the errors in the atmospheric circulation models and also they cause errors in the prediction and evaluation of the earth’s future climate. To solve this problem, the outputs of general circulation models are down-scaled through dynamical and statistical methods. In recent years, from the various methods of downscaling, researchers have been interested in the statistical downscaling method more than other methods. In the statistical downscaling, statistical methods such as regression and air generator models can be used. The statistical downscaling methods which also include the SDSM model, do the reducing scale based on the statistical history of large-scale predictors and the dependent variables. One of the most widely used models for downscaling GCM data, is the statistical model SDSM. In this study, the competency of this model for downscaling mean temperature was evaluated in Kermanshah station. Several data series including the mean daily temperature in Kermanshah station, data from the function of the national center for environmental prediction and the data from HadCM3 general circulation models were used under the A2 and B2 scenarios. Based on the A2 scenario a world is imagined in which the countries are operating independently, they are self-reliant, the world's population constantly increases, and economic development is region-based. And according to the B2 scenario, the population steadily increases but its growth rate is lower than the A2. The emphasis is on local solutions rather than having global solutions for economic, environmental and social stability, moderate economic development and Rapid technological changes. Kermanshah station data includes daily average from the beginning of 1961 until the end of 2010 which were used for calibration of the model. To this end, collecting the independent variables and the calibration of the model were done for the mean temperature by applying the daily temperature data of Kermanshah’s synoptic station and the data from the National Center for Environmental Prediction. In order to calibrate the observed data from Kermanshah’s station and the data from the National Center for Environmental Prediction (NCEP), it was divided into two 15-year periods (1975, 1961) and (1990 to 1976). The first 15 years was used to calibrate the model using the least square error method optimization. This work was done for the period of 40 years from 1961 to 2000. Then the mean temperature for the 10-year period 2010 -2001 data based on two basic periods of 15 years (1990-1961) and the 40 years (2000- 1961) under the two scenarios A2 and B2, were Predicted and were compared with the observed data of this period to evaluate the predicting performance of the model. The results of the evaluation period (2000-1961 and 1990-1976) using NCEP data showed that the SDSM model has an acceptable capability in simulating the variables such as the mean temperature in the evaluation period and the basic. It should be noted that with an increase in the prediction base period to 40 years, the differences according to the NCEP model and the observed data turned to zero. This can be considered as one of the model’s defects which is due to the use of linear regression because, by reducing the base period to simulate the mean temperature, the results of it, falls away from the average of the observed period, but by increasing the period duration, the outcomes will be valid. Also the amount of variance, the maximum and minimum temperature which are applied by the model to calculate the mean temperature, are not suitable and competence and it commits several errors. This can be caused by poor capability of the model to evaluate and reveal temperature fluctuations; this could be the consequence of adherence to linear regression of the model, although the station’s local conditions and the Hadcm3 model’s errors could intensify the inability.
Yunes Khosravi; Hassan Lashkari; Aliakbar Matkan; Hossein Asakareh
Abstract
Introduction
Survey of spatial relationships of environmental data is considered as one of themost important goalsof spatial statistics for analyzing the spatial patterns and understanding the spatial dependencies. In this context, the Exploratory Spatial Data Analysis (ESDA) could well provide methods ...
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Introduction
Survey of spatial relationships of environmental data is considered as one of themost important goalsof spatial statistics for analyzing the spatial patterns and understanding the spatial dependencies. In this context, the Exploratory Spatial Data Analysis (ESDA) could well provide methods for distinguishing betweenspatialrandomandnon-random patterns. Using the ESDA for analyzing the spatial autocorrelation of climatic elements is necessary to distinguish their changes and spatial distribution. The present research is aimed at explaining the use of ESDA to describethe spatial patterns ofwater vapor pressureas one of the most important climatic parameters. Water vapor pressure plays a crucial role in climate system as an important feedback variable associated with the earth’s energy balance and hydrologic cycle. This climatic parameter has an important rolein explaining the climate change or changes in climatic parameters, because: 1) It is the main sourceof rainfall in allweathersystems, 2) It suppliesthe latent heatin this process and controls the heat inthetroposphere, 3) It is the booster of the storm's speed and 4) It plays a major role in the dynamics of atmospheric circulation. So, determination and interpretation of the likely reasons of Water vapor pressure changes and its variability are vitally important for human as well as other living-beings.
Materials & Methods
The studied area, with about 360,200 km2 area, is located in the South and the Southwest of Iran and approximately between 25° 00'N and 34° 25'N latitudes and between 45° 38'E and 59° 17'E longitudes. Southern and southwestern parts of the studied area are located beside two massive sources of moisture, i.e. Persian Gulf and Oman Sea. The main mountain chain in the studied area is Zagros that extends from the northwest to the Southern part of the studied area. The Zagros mountainrange is responsible for the major portion of rain-producing air masses that enter the region from the Western and Northwestern sides, with relatively high amounts of rainfall. In this study, water vapor pressure data in pixels (dimension of 9×9 km) inthe time interval of 1981-2010 were collected by the Iranian Meteorological data website (http://www.weather.ir).To interpolate the water vapor pressure, Kriging Inverse Distance Weighting (IDW) and Radial Basis Functions (RBF) were tested and so after theerror validation, the optimum method (Ordinary Kriging with Gaussian method) was chosen. Considering the aim of this study, analyzing the spatial variability of WV in regional and local scale, the most important geographical features such as elevation, longitude, latitude, slope and other aspects were chosen. Topographical maps of the studied area were collected by the Geological Survey of Iran (http://www.gsi.ir). The Digital Elevation Model (DEM)with a 10 Km cell size was derived by mosaicking, geo-referencing, and editing these maps in Arc GIS 10.2 software, and the geographical features were prepared based on it. Moran's I, local Moran'sAnselin, and LISA were used asESDA’s approaches to analyze the spatial autocorrelation of water vapor pressure patterns based on climate parameters.
Results & Discussion
According to the cross validation, it was cleared thatthe optimum method for interpolation of water vapor pressureis Ordinary Kriging with Gaussian method. The results of Moran’s Istatistic showed that the water vapor pressure hasspatial structure and is distributed in cluster patternin the South and the Southwest of Iran. The monthly surveys showed thatthe spatial autocorrelation of water vapor pressure in warm months is higher than the cold months and therefore hasa greater tendency to cluster. The results alsoshowedthat the water vapor pressure is tending to disperse and non-clusterinspace in the South and SouthWest of Iran. The bivariate Moran's Istatistic for relation of water vapor pressure and longitude showed thestrong and positive spatial autocorrelation and also clustered pattern.
Conclusion
The monthly surveys showed that the spatial autocorrelation of water vapor pressure in warm months is higher than the cold months and is more tending towards clustering. The existence of such situation in most regions of the studied area in the warm seasons reflects the consistency and homogeneity in this seasons in relation to other seasons. The main reason for these circumstances may be the lack of non-diversification of input pressure systems in these seasons, climate uniformity and sustainability and effects of local systems. Over the time, the water vapor pressure in the South and Southwest of Iran has tended to be more dispersed and less clustering in space. The reason for this incident is not fully revealed but it may be attributed to topographical effects, changes in system positioning, land use changes, etc.Investigating the relationship between spatial distribution of water vapor pressure and geographical parameters showed that the relationship betweenwater vapor pressureand latitude,elevation and slope suggested adispersed and heterogeneousspatial distribution between them. The results of the bivariaterelationship betweenwater vapor pressureand other aspects suggested a discontinuous and random relation.
Hossein Asakareh; Saeedeh Ashrafi
Volume 20, Issue 80 , February 2012, , Pages 13-17
Abstract
Climatic phenomena are complex. The complexity is that the phenomenon has many causes. The interrelationships between climatic elements and factors have led to the application of statistical methods, including multivariate statistical methods in climatic studies. One of these applications is to study ...
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Climatic phenomena are complex. The complexity is that the phenomenon has many causes. The interrelationships between climatic elements and factors have led to the application of statistical methods, including multivariate statistical methods in climatic studies. One of these applications is to study and predict the relationship between climatic elements and factors. Application of statistical methods facilitates these studies. In this study, the relationship between the annual relative humidity and annual temperature with the number of annual days of precipitation of Zanjan station during the statistical period of 1956-2005 have been investigated. For this purpose, Pearson correlation methods have been used. Correlation means the covariance of two variables. The results show that the correlation coefficients of the variables are significant. furthermore, simple linear regression is used to estimate the number of annual days of precipitation with respect to annual relative humidity. Two-variable regression will be used to estimate the number of annual days of precipitation with respect to annual relative humidity and annual temperature.
Hossein Asakereh; Soheila Maleki
Volume 20, Issue 78 , August 2011, , Pages 7-12
Abstract
Temperature and precipitation are two important climatic variables that have a significant effect on life and activities of individuals. These two elements are generally dependent on each other. In this research, the correlation between temperature and precipitation is determined using Pearson correlation ...
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Temperature and precipitation are two important climatic variables that have a significant effect on life and activities of individuals. These two elements are generally dependent on each other. In this research, the correlation between temperature and precipitation is determined using Pearson correlation coefficient. The existence or absence of a linear relationship between temperature and precipitation was also examined. Furthermore, the simultaneous effect of temperature and relative humidity on rainfall was calculated and the significance of regression was investigated. In this study, SPSS software was used for drawing graphs and multivariate regression analysis. Using the findings of this research, it was shown that there is a weak inverse relation between temperature and precipitation. The actual contribution of temperature changes in precipitation is 3.61% which is very low, and there is no linear relationship between temperature and precipitation. In the two-variable regression, the temperature had again no significant effect on rainfall, but relative humidity was an effective variable in precipitation of this station. In this study, the mean annual temperature and precipitation of Zanjan station, extracted from the meteorological website, have been used during the statistical period of 1956-2005. Zanjan is located on the northern 36o41’ and eastern 29o48’ in the northwest of Iran. The city’s altitude at the station is 1620 meters.
Hossein Asakareh; Mohammad Savari
Volume 19, Issue 75 , November 2010, , Pages 92-96
Abstract
According to statistics thousands of people die or become disabled in road accidents every year. Several factors may play a role in the occurrence of accidents, among which are road geometric design, environmental conditions and human factors. Among the environmental factors that affect the safety and ...
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According to statistics thousands of people die or become disabled in road accidents every year. Several factors may play a role in the occurrence of accidents, among which are road geometric design, environmental conditions and human factors. Among the environmental factors that affect the safety and sustainability of transportation, we can mention the role of climatic phenomena such as precipitation, wind, temperature, fog, dust and humidity. The axis of Ahwaz-Susangerd, 55 km long, is among the main roads linking to Hamidieh, Susangerd, Hoveizeh and Bostan through Ahwaz. In this study, the analysis of road accidents has been conducted with climatic attitude and in order to investigate the relationship between spatial distribution of accidents and elements-climatic phenomena such as precipitation, fog and dust, temperature, humidity and wind. In order to investigate the role of climatic phenomena in the occurrence of accidents, the hourly meteorological data of Ahwaz, Abadan, Bostan and Hamidieh weather stations have been used and the meteorological situation of the moment of accident has been extracted through interpolation of these data. Also, police information of road accidents has been used. The road map of Southwest of Khuzestan with a scale of 250,000: 1 has been selected as the base map. Using these data for a three-year period (2005-2007), a map of dispersion of accidents in different atmospheric conditions is provided. Based on the results from the maps of accident risk, the highest probability of accident risk during rain falls is in 9, 16, 21, 22 and 25, fog and dust in 10, 29, 35 and 49, the maximum temperature in 20, 25, 35, 43 and 49, wind in 10, 21, 22 and 43, and humidity in 9, 35, 45 and 50 kilometers of the road.
Hosein Asakareh
Volume 12, Issue 48 , February 2003, , Pages 20-24
Abstract
No region is independent from the point of view of climate, and the dominant climatic pattern in one region affects other regions, because there is a systematic atmospheric relationship between them in spite of climatic disparities between different regions; for instance changes occurring in tropical ...
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No region is independent from the point of view of climate, and the dominant climatic pattern in one region affects other regions, because there is a systematic atmospheric relationship between them in spite of climatic disparities between different regions; for instance changes occurring in tropical regions cause atmospheric differences between the areas of mid-latitude and other areas.In general, there is always a completely distinct relationship between dominant weather conditions in high latitudes and those of low latitudes. Therefore, the earth's atmosphere works as a single system, in such a way that the change of air circulation from an area of the northern hemisphere to other parts affects the upstream or downstream of that region (Busher, translated by Qaemi, 1994).
The purpose of this paper is to present the variable state of air circulation, help provide a relative knowledge of climate change and unusual situations of pressure and air flow in the North Atlantic. The behavior of pressure systems in the Atlantic Ocean affect each other, and on the other hand they can also affect the climate of Iran directly or indirectly (Alijani, 1987). Hence, the knowledge of the status of these systems will be a basis for understanding the origin of some climatic behaviors in Iran. In order to understand the synoptic patterns generated from different phases of the North Atlantic oscillation, pressure distribution and abnormalities on the surface of the earth and in the Atlantic Ocean have been considered.